{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a dynamical system\n",
    "\n",
    "Topics:\n",
    "* What *is* a dynamical system in the context of **DynamicalSystems.jl**?\n",
    "* How can we define a discrete or continuous system?\n",
    "* What are the differences in handling large and small systems?\n",
    "    * Handy Dandy definition table!\n",
    "* Comment on DifferentialEquations.jl\n",
    "* Docstrings\n",
    "\n",
    "---\n",
    "\n",
    "# Dynamical System\n",
    "\n",
    "A \"dynamical system\" is a law that describes how some variables should evolve in time. This law is described by the \"equations of motion\" function $\\vec{f}$.\n",
    "\n",
    "There are two types of dynamical systems (in our case):\n",
    "\n",
    "1. One is called a map, where time is a discrete quantity (like \"steps\" or \"generations\"). The equations of motion then look something like $\\vec{u}_{n+1} = \\vec{f}(\\vec{u}_n, p, n)$ where $n$ is an integer and $p$ are the parameters of the system.\n",
    "\n",
    "2. The other type is called an Ordinary Differential Equation (ODE), where time is a continuous quantity. Then, the equations of motion give the time derivatives of the variables: $\\frac{d\\vec{u}}{dt} = \\vec{f}(\\vec{u}, p, t)$.\n",
    "\n",
    "In both cases $\\vec{u}$ is the state of the system, a vector of the variables that define the system. For example, $\\vec{u}$ may be defined as $(x, y, z)$, or as $(\\theta, I)$, or as $(V, I, R, C_1, C_2)$, etc.\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a simple discrete system\n",
    "\n",
    "\n",
    "For all intents and purposes, a `DynamicalSystem` is a Julia `struct` that contains all the information of a dynamical system:\n",
    "1. Equations of motion function $\\vec{f}$\n",
    "3. Initial state.\n",
    "4. Parameter container (if the equations of motion are parameterized).\n",
    "2. *Optional* : Jacobian of the equations of motion.\n",
    "\n",
    "\n",
    "For simplicity let's focus on creating a simple discrete chaotic system, the [Hénon map](https://en.wikipedia.org/wiki/H%C3%A9non_map). \n",
    "\n",
    "The constructor we need is:\n",
    "\n",
    "```julia\n",
    "DiscreteDynamicalSystem(eom, state, p)\n",
    "```\n",
    "\n",
    "In order to construct a Hénon map, let's discuss the input arguments we need to pass to this constructor!\n",
    "\n",
    "#### 1. Equations of motion\n",
    "The first argument of the above constructor --`eom` -- is a **function** representing the equations of motion.\n",
    "\n",
    "Therefore, the first step in creating a `DynamicalSystem` is to define the equations of motion. For the Hénon map they have the form:\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "x_{n+1} &= 1 - ax^2_n+y_n \\\\\n",
    "y_{n+1} & = bx_n\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "Now we must make a function out of them. There is some strictness when writing this function in Julia. Specifically, it can only be one of two forms: out-of-place (oop) or in-place (iip). Here is how to define it:\n",
    "\n",
    "* **oop** : The `eom` function **must** be in the form `eom(x, p, t) -> SVector`\n",
    "  which means that given a state `x::SVector` and some parameter container\n",
    "  `p` it returns a [`SVector`](http://juliaarrays.github.io/StaticArrays.jl/stable/pages/api.html#SVector-1)\n",
    "  (from the [StaticArrays](https://github.com/JuliaArrays/StaticArrays.jl) module)\n",
    "  containing the next state.\n",
    "* **iip** : The `eom` function **must** be in the form `eom!(xnew, x, p, t)`\n",
    "  which means that given a state `x::Vector` and some parameter container `p`,\n",
    "  it writes in-place the new state in `xnew`.\n",
    "\n",
    "We advise to use **oop** for systems with dimension < 11, and use **iip** otherwise.\n",
    "\n",
    "*If you are familiar with [DifferentialEquations.jl](http://docs.juliadiffeq.org/latest/), then notice that the equations of motion are defined in an identical manner*\n",
    "\n",
    "Because the Hénon map is only 2-dimensional, we follow the advice of the documentation and take advantage of the [`StaticArrays`](https://github.com/JuliaArrays/StaticArrays.jl) module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h_eom (generic function with 1 method)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "using DynamicalSystems\n",
    "h_eom(x, p, t) = SVector{2}(1.0 - p[1]*x[1]^2 + x[2], p[2]*x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Remember: *both* `p` (for parameters) and `t` (for time) must be included in the equations of motion function, irrespective of whether they are used or not!\n",
    "\n",
    "#### 2. State\n",
    "\n",
    "The second argument for the `DiscreteDynamicalSystem` constructor is a `state`, which represents the initial condition for the system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element Array{Float64,1}:\n",
       " 0.0\n",
       " 0.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state = zeros(2) # doesn't matter if we use Vector of SVector for the `state`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Parameters for our EOM\n",
    "\n",
    "Then, the last argument, `p`, is simply a parameter container."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element Array{Float64,1}:\n",
       " 1.4\n",
       " 0.3"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = [1.4, 0.3] # p = [a, b] from the equations of motion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These 3 things are enough to make a `DynamicalSystem`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-dimensional discrete dynamical system\n",
       " state:     [0.0, 0.0]\n",
       " e.o.m.:    h_eom\n",
       " in-place?  false\n",
       " jacobian:  ForwardDiff\n"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "henon = DiscreteDynamicalSystem(h_eom, state, p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting a trajectory\n",
    "\n",
    "Now that we have created the system, the first (and most basic) thing to do is to simply plot its time evolution and see what it looks like. \n",
    "\n",
    "The `trajectory` function is a convenient tool that evolves the system and returns the output at equally spaced time intervals. The call signature is simply:\n",
    "\n",
    "```julia\n",
    "trajectory(ds::DynamicalSystem, T [, u]; kwargs...)\n",
    "```\n",
    "which evolves a system for total time `T`, optionally starting from a different state `u`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-dimensional Dataset{Float64} with 100001 points\n",
       "  0.0        0.0      \n",
       "  1.0        0.0      \n",
       " -0.4        0.3      \n",
       "  1.076     -0.12     \n",
       " -0.740886   0.3228   \n",
       "  0.554322  -0.222266 \n",
       "  0.347552   0.166297 \n",
       "  0.997188   0.104265 \n",
       " -0.287871   0.299156 \n",
       "  1.18314   -0.0863614\n",
       " -1.0461     0.354942 \n",
       " -0.177128  -0.313831 \n",
       "  0.642244  -0.0531385\n",
       "  ⋮                   \n",
       "  0.582553   0.103    \n",
       "  0.627885   0.174766 \n",
       "  0.622831   0.188365 \n",
       "  0.64528    0.186849 \n",
       "  0.603908   0.193584 \n",
       "  0.682997   0.181173 \n",
       "  0.528095   0.204899 \n",
       "  0.814462   0.158428 \n",
       "  0.229742   0.244338 \n",
       "  1.17044    0.0689225\n",
       " -0.848995   0.351133 \n",
       "  0.342024  -0.254698 \n"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trajectory from initial condition\n",
    "tr = trajectory(henon, 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-dimensional Dataset{Float64} with 100001 points\n",
       "  0.00246317   0.0019625  \n",
       "  1.00195      0.000738951\n",
       " -0.404738     0.300586   \n",
       "  1.07125     -0.121421   \n",
       " -0.728024     0.321375   \n",
       "  0.579347    -0.218407   \n",
       "  0.311692     0.173804   \n",
       "  1.03779      0.0935077  \n",
       " -0.414307     0.311337   \n",
       "  1.07103     -0.124292   \n",
       " -0.73023      0.321308   \n",
       "  0.574777    -0.219069   \n",
       "  0.318414     0.172433   \n",
       "  ⋮                       \n",
       " -1.13066      0.363665   \n",
       " -0.42609     -0.339199   \n",
       "  0.406628    -0.127827   \n",
       "  0.640689     0.121988   \n",
       "  0.547314     0.192207   \n",
       "  0.772834     0.164194   \n",
       "  0.328014     0.23185    \n",
       "  1.08122      0.0984041  \n",
       " -0.538247     0.324366   \n",
       "  0.918772    -0.161474   \n",
       " -0.343273     0.275632   \n",
       "  1.11066     -0.102982   \n"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trajectory from a different starting point\n",
    "tr2 = trajectory(henon, 100000, 0.01rand(2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 600x400 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "using PyPlot\n",
    "figure(figsize=(6,4))\n",
    "plot(tr[:, 1], tr[:, 2], lw = 0.0, marker = \"o\", ms = 0.1, alpha = 0.5);\n",
    "plot(tr2[:, 1], tr2[:, 2], lw = 0.0, marker = \"o\", ms = 0.1, alpha = 0.5);\n",
    "xlabel(\"x\"); ylabel(\"y\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crash-course on `Dataset`\n",
    "\n",
    "`trajectory` returns an object that is a `Dataset`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DynamicalSystemsBase.Dataset{2,Float64}\n",
      "2-dimensional Dataset{Float64} with 100001 points\n"
     ]
    }
   ],
   "source": [
    "tr = trajectory(henon, 100000)\n",
    "println(typeof(tr))\n",
    "println(summary(tr))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Dataset` instances handle most data in **DynamicalSystems.jl**. `Dataset` is a wrapper of a `Vector` of `SVector`s (statically sized vectors)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array{StaticArrays.SArray{Tuple{2},Float64,1,2},1}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "typeof(tr.data) # this is where the `Dataset` has the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When accessed with one index, a `Dataset` behaves as a vector of vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-element StaticArrays.SArray{Tuple{2},Float64,1,2}:\n",
       " 0.0\n",
       " 0.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr[1] # variables at first time point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5-element Array{StaticArrays.SArray{Tuple{2},Float64,1,2},1}:\n",
       " [0.0, 0.0]         \n",
       " [1.0, 0.0]         \n",
       " [-0.4, 0.3]        \n",
       " [1.076, -0.12]     \n",
       " [-0.740886, 0.3228]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr[1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When accessed with two indices, a `Dataset` behaves like a matrix with each column being the timeseries of each dynamic variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100001-element Array{Float64,1}:\n",
       "  0.0     \n",
       "  1.0     \n",
       " -0.4     \n",
       "  1.076   \n",
       " -0.740886\n",
       "  0.554322\n",
       "  0.347552\n",
       "  0.997188\n",
       " -0.287871\n",
       "  1.18314 \n",
       " -1.0461  \n",
       " -0.177128\n",
       "  0.642244\n",
       "  ⋮       \n",
       "  0.582553\n",
       "  0.627885\n",
       "  0.622831\n",
       "  0.64528 \n",
       "  0.603908\n",
       "  0.682997\n",
       "  0.528095\n",
       "  0.814462\n",
       "  0.229742\n",
       "  1.17044 \n",
       " -0.848995\n",
       "  0.342024"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr[:, 1] # timeseries of first variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56-element Array{Float64,1}:\n",
       "  0.0      \n",
       "  0.0      \n",
       "  0.3      \n",
       " -0.12     \n",
       "  0.3228   \n",
       " -0.222266 \n",
       "  0.166297 \n",
       "  0.104265 \n",
       "  0.299156 \n",
       " -0.0863614\n",
       "  0.354942 \n",
       " -0.313831 \n",
       " -0.0531385\n",
       "  ⋮        \n",
       "  0.128109 \n",
       "  0.186576 \n",
       "  0.175984 \n",
       "  0.211444 \n",
       "  0.144155 \n",
       "  0.266456 \n",
       "  0.0119177\n",
       "  0.379274 \n",
       " -0.367719 \n",
       " -0.217233 \n",
       " -0.0305363\n",
       "  0.230479 "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr[1:56, 2] # time points 1:56 of second variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "-0.17712836162447815"
      ],
      "text/plain": [
       "-0.17712836162447815"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr[12, 1] # value of first variable at 12th timepoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-dimensional Dataset{Float64} with 10 points\n",
       "  0.0        0.0      \n",
       "  1.0        0.0      \n",
       " -0.4        0.3      \n",
       "  1.076     -0.12     \n",
       " -0.740886   0.3228   \n",
       "  0.554322  -0.222266 \n",
       "  0.347552   0.166297 \n",
       "  0.997188   0.104265 \n",
       " -0.287871   0.299156 \n",
       "  1.18314   -0.0863614\n"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr[1:10, 1:2] # using two ranges returns a `Dataset`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adding a Jacobian\n",
    "\n",
    "Let's see `henon` again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-dimensional discrete dynamical system\n",
       " state:     [0.0, 0.0]\n",
       " e.o.m.:    h_eom\n",
       " in-place?  false\n",
       " jacobian:  ForwardDiff\n"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "henon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "* The last line, \"`jacobian:  ForwardDiff`\"\n",
    "says that the Jacobian function of the equations of motion was computed automatically using the module [`ForwardDiff`](http://www.juliadiff.org/ForwardDiff.jl/stable/user/api.html). \n",
    "* The Jacobian function is a crucial component of a dynamical system, and that is why if it is not given, it is computed automatically.\n",
    "\n",
    "\n",
    "\n",
    "Even though the automatic computation is very efficient, the best possible performance will come if you pass a \"hard-coded\" jacobian:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "h_jacobian (generic function with 1 method)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_jacobian(x, p, t) = @SMatrix [-2*p[1]*x[1] 1.0; p[2] 0.0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Notice that for **out-of-place** systems, the Jacobian must also have the same form as the equations of motion, namely to return an `SMatrix`.\n",
    "\n",
    "Now, we can pass this Jacobian function to the `DiscreteDynamicalSystem` constructor as a 4th argument:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2-dimensional discrete dynamical system\n",
       " state:     [0.0, 0.0]\n",
       " e.o.m.:    h_eom\n",
       " in-place?  false\n",
       " jacobian:  h_jacobian\n"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "henon_with_jac = DiscreteDynamicalSystem(h_eom, state, p, h_jacobian)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see the difference in performance, let's call this Jacobian function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  2.560 ns (0 allocations: 0 bytes)\n",
      "  17.066 ns (0 allocations: 0 bytes)\n"
     ]
    }
   ],
   "source": [
    "using BenchmarkTools\n",
    "a = rand(SVector{2})\n",
    "@btime $(henon_with_jac.jacobian)($a, $henon.prob.p, 0);\n",
    "@btime $(henon.jacobian)($a, $henon.prob.p, 0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that even though `ForwardDiff` is truly performant, the hard-coded version is much faster."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Continuous System\n",
    "* The process of creating a continuous system is identical to that of a discrete system, except that the constructor `ContinuousDynamicalSystem` is used instead. \n",
    "* In this case the function `eom` returns the time derivatives and not a \"next state\".\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "* We will take the opportunity to show the process of using in-place equations of motion for a continuous system, which is aimed to be used for large systems (dimensionality $\\ge$ 10).\n",
    "\n",
    "* In addition, the system we will use (Henon-Heiles) does not have any parameters.\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\dot{x} &= p_x \\\\\n",
    "\\dot{y} &= p_y \\\\\n",
    "\\dot{p}_x &= -x -2 xy \\\\\n",
    "\\dot{p}_y &= -y - (x^2 - y^2)\n",
    "\\end{aligned}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-dimensional continuous dynamical system\n",
       " state:     [0.0, -0.25, 0.42081, 0.0]\n",
       " e.o.m.:    hheom!\n",
       " in-place?  true\n",
       " jacobian:  ForwardDiff\n"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Henon-heiles famous system\n",
    "# in-place form of equations of motion\n",
    "# du is the derivatives vector, u is the state vector\n",
    "function hheom!(du, u, p, t)\n",
    "    du[1] = u[3]\n",
    "    du[2] = u[4]\n",
    "    du[3] = -u[1] - 2u[1]*u[2]\n",
    "    du[4] = -u[2] - (u[1]^2 - u[2]^2)\n",
    "    return nothing\n",
    "end\n",
    "\n",
    "# pass `nothing` as the parameters, because the system doesn't have any\n",
    "hh = ContinuousDynamicalSystem(hheom!, [0, -0.25, 0.42081, 0], nothing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, now we can get a trajectory of this system, by solving using DifferentialEquations.jl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-dimensional Dataset{Float64} with 2001 points\n",
       "  0.0         -0.25      0.42081   0.0      \n",
       "  0.0210361   -0.24961   0.420547  0.0156079\n",
       "  0.0420459   -0.248441  0.419755  0.0311131\n",
       "  0.0630027   -0.246502  0.418429  0.0464137\n",
       "  0.0838796   -0.243805  0.416557  0.0614095\n",
       "  0.104649    -0.240367  0.414125  0.0760025\n",
       "  0.125283    -0.236213  0.411116  0.0900977\n",
       "  0.145751    -0.231368  0.407508  0.103604 \n",
       "  0.166023    -0.225864  0.403277  0.116435 \n",
       "  0.186068    -0.219737  0.398396  0.128509 \n",
       "  0.205851    -0.213026  0.392838  0.139751 \n",
       "  0.22534     -0.205777  0.386572  0.150091 \n",
       "  0.244496    -0.198034  0.379568  0.159465 \n",
       "  ⋮                                         \n",
       " -0.116658    -0.263007  0.370702  0.157562 \n",
       " -0.098057    -0.254734  0.373289  0.173284 \n",
       " -0.0793356   -0.245687  0.375503  0.188535 \n",
       " -0.0605135   -0.23589   0.377313  0.203232 \n",
       " -0.0416116   -0.225374  0.378687  0.217296 \n",
       " -0.0226527   -0.214172  0.379586  0.230651 \n",
       " -0.00366163  -0.202322  0.379968  0.243226 \n",
       "  0.0153347   -0.189864  0.379788  0.254954 \n",
       "  0.034307    -0.176842  0.379     0.265774 \n",
       "  0.0532238   -0.163303  0.377554  0.27563  \n",
       "  0.0720506   -0.149296  0.375399  0.284472 \n",
       "  0.090751    -0.134873  0.372483  0.292256 \n"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr = trajectory(hh, 100.0, dt = 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "PyPlot.Figure(PyObject <Figure size 600x400 with 1 Axes>)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize = (6,4))\n",
    "plot(tr[:, 1], tr[:, 2]);\n",
    "xlabel(\"\\$q_1\\$\"); ylabel(\"\\$q_2\\$\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handy Dandy definition table\n",
    "\n",
    "Depending on whether your system is small or large, you want to use out-of-place or in-place equations of motion. The Jacobian function (if you wish to provide it) must also be of the same form. \n",
    "\n",
    "Here is a handy table that summarizes what the definitions should look like:\n",
    "\n",
    "|          System Type         |    equations of motion    |            Jacobian            |\n",
    "|:----------------------------:|:-------------------------:|:------------------------------:|\n",
    "| in-place (big systems)       | `eom!(du, u, p, t)`       | `jacobian!(J, u, p, t)`        |\n",
    "| out-of-place (small systems) | `eom(u, p, t) -> SVector` | `jacobian(u, p, t) -> SMatrix` |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Time-evolution of systems & DifferentialEquations.jl\n",
    "Discrete systems are evolved using internal algorithms. However, all time evolution of continuous systems is done through the [DifferentialEquations.jl](http://docs.juliadiffeq.org/latest/) library. In fact, `trajectory` for continuous systems simply wraps `solve` with some extra arguments.\n",
    "\n",
    "Keep in mind that by default all continuous systems are evolved using the solver `Vern9()` (9th order Verner solver) with tolerances `abstol = reltol = 1e-9`. This is especially important when one wants to compare benchmarks with different packages.\n",
    "\n",
    "It is almost certain that if you use **DynamicalSystems.jl** you want to use also DifferentialEquations.jl, due to the huge list of available features.\n",
    "\n",
    "\n",
    "## When to use **DynamicalSystems.jl**?\n",
    "At this point in the tutorial you might be thinking:\n",
    "> How is DynamicalSystems.jl different from DifferentialEquations.jl? Seems the same to me so far...\n",
    "\n",
    "That's because we haven't seen any special features of **DynamicalSystems.jl** yet! `trajectory` is nothing more than a \"convenient shortcut\".\n",
    "\n",
    "The [contents page](https://juliadynamics.github.io/DynamicalSystems.jl/latest/#contents) of the documentation does a good job describing what is possible through **DynamicalSystems.jl**.\n",
    "\n",
    "In addition, for usage with DifferentialEquations.jl, we can create specialized integrators that evolve the system and the tangent space, or many states of the system in parallel (at *exactly* same times):\n",
    "* `tangent_integrator`\n",
    "* `parallel_integrator`\n",
    "\n",
    "These functions work for both continuous and discrete systems and also work regardless of whether the system is in-place (large), out-of-place (small) or auto-differentiated. Special attention has also been given to the performance of the integrators. \n",
    "\n",
    "Also, keep in mind that regardless of whether you use a `DynamicalSystem` to produce a timeseries or not, more than half of the **DynamicalSystems.jl** library is using numerical data as an input. Most of the time this numerical data is expected in the form of an `AbstractDataset` instance, which is what `trajectory` returns.\n",
    "\n",
    "## Using DifferentialEquations.jl from a `DynamicalSystem`\n",
    "\n",
    "It is *very* likely that other features of DifferentialEquations.jl will be useful to someone using **DynamicalSystems.jl**. However, you can still use DifferentialEquations.jl *after* you have defined a continuous dynamical system, because the field `prob` gives an `ODEProblem`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DiffEqBase.ODEProblem with uType Array{Float64,1} and tType Float64. In-place: true\n",
       "timespan: (0.0, Inf)\n",
       "u0: [0.0, -0.25, 0.42081, 0.0]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hh.prob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **please be careful when using this problem directly, because as you can see the `tspan[end]` field is `Inf`!!!**\n",
    "\n",
    "\n",
    "* One final comment: using Callbacks is *not* possible with **DynamicalSystems.jl**, because the the equations of motion function has to be assumed differentiable \"everywhere\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Docstrings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: \u001b[1mD\u001b[22m\u001b[1my\u001b[22m\u001b[1mn\u001b[22m\u001b[1ma\u001b[22m\u001b[1mm\u001b[22m\u001b[1mi\u001b[22m\u001b[1mc\u001b[22m\u001b[1ma\u001b[22m\u001b[1ml\u001b[22m\u001b[1mS\u001b[22m\u001b[1my\u001b[22m\u001b[1ms\u001b[22m\u001b[1mt\u001b[22m\u001b[1me\u001b[22m\u001b[1mm\u001b[22m \u001b[1mD\u001b[22m\u001b[1my\u001b[22m\u001b[1mn\u001b[22m\u001b[1ma\u001b[22m\u001b[1mm\u001b[22m\u001b[1mi\u001b[22m\u001b[1mc\u001b[22m\u001b[1ma\u001b[22m\u001b[1ml\u001b[22m\u001b[1mS\u001b[22m\u001b[1my\u001b[22m\u001b[1ms\u001b[22m\u001b[1mt\u001b[22m\u001b[1me\u001b[22m\u001b[1mm\u001b[22ms \u001b[1mD\u001b[22m\u001b[1my\u001b[22m\u001b[1mn\u001b[22m\u001b[1ma\u001b[22m\u001b[1mm\u001b[22m\u001b[1mi\u001b[22m\u001b[1mc\u001b[22m\u001b[1ma\u001b[22m\u001b[1ml\u001b[22m\u001b[1mS\u001b[22m\u001b[1my\u001b[22m\u001b[1ms\u001b[22m\u001b[1mt\u001b[22m\u001b[1me\u001b[22m\u001b[1mm\u001b[22msBase\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "```\n",
       "DynamicalSystem\n",
       "```\n",
       "\n",
       "The central structure of **DynamicalSystems.jl**. All functions of the suite that handle systems \"analytically\" (in the sense that they can use known equations of motion) expect an instance of this type.\n",
       "\n",
       "Contains a problem defining the system (field `prob`), the jacobian function (field `jacobian`) and the initialized Jacobian matrix (field `J`).\n",
       "\n",
       "## Constructing a `DynamicalSystem`\n",
       "\n",
       "```julia\n",
       "DiscreteDynamicalSystem(eom, state, p [, jacobian [, J0]]; t0::Int = 0)\n",
       "ContinuousDynamicalSystem(eom, state, p [, jacobian [, J0]]; t0 = 0.0)\n",
       "```\n",
       "\n",
       "with `eom` the equations of motion function. `p` is a parameter container, which we highly suggest to use a mutable object like `Array`, [`LMArray`](https://github.com/JuliaDiffEq/LabelledArrays.jl) or a dictionary. Pass `nothing` in the place of `p` if your system does not have parameters. With these constructors you also do not need to provide some final time, since it is not used by **DynamicalSystems.jl** in any manner.\n",
       "\n",
       "`t0`, `J0` allow you to choose the initial time and provide an initialized Jacobian matrix.\n",
       "\n",
       "Continuous system solvers use [**DifferentialEquations.jl**](http://docs.juliadiffeq.org/latest/) and by default are integrated with a 9th order Verner solver `Vern9()` with tolerances `abstol = reltol = 1e-9`.\n",
       "\n",
       "### Equations of motion\n",
       "\n",
       "The are two \"versions\" for `DynamicalSystem`, depending on whether the equations of motion (`eom`) are in-place (iip) or out-of-place (oop). Here is how to define them:\n",
       "\n",
       "  * **oop** : The `eom` **must** be in the form `eom(x, p, t) -> SVector` which means that given a state `x::SVector` and some parameter container `p` it returns an [`SVector`](http://juliaarrays.github.io/StaticArrays.jl/stable/pages/api.html#SVector-1) (from the [StaticArrays](https://github.com/JuliaArrays/StaticArrays.jl) module) containing the next state.\n",
       "  * **iip** : The `eom` **must** be in the form `eom!(xnew, x, p, t)` which means that given a state `x::Vector` and some parameter container `p`, it writes in-place the new state in `xnew`.\n",
       "\n",
       "`t` stands for time (integer for discrete systems). iip is suggested for big systems, whereas oop is suggested for small systems. The break-even point at around 100 dimensions, and for using functions that use the tangent space (like e.g. `lyapunovs` or `gali`), the break-even point is at around 10 dimensions.\n",
       "\n",
       "The constructor deduces automatically whether `eom` is iip or oop. It is not possible however to deduce whether the system is continuous or discrete just from the equations of motion, hence the 2 constructors.\n",
       "\n",
       "### Jacobian\n",
       "\n",
       "The optional argument `jacobian` for the constructors is a *function* and (if given) must also be of the same form as the `eom`, `jacobian(x, p, n) -> SMatrix` for the out-of-place version and `jacobian!(xnew, x, p, n)` for the in-place version.\n",
       "\n",
       "If `jacobian` is not given, it is constructed automatically using the module [`ForwardDiff`](http://www.juliadiff.org/ForwardDiff.jl/stable/).\n",
       "\n",
       "### Getting a `Solution` struct\n",
       "\n",
       "The continuous constructor creates a standard [ODEProblem](http://docs.juliadiffeq.org/latest/types/ode_types.html) from [**DifferentialEquations.jl**](http://docs.juliadiffeq.org/latest/).\n",
       "\n",
       "You can *always* take advantage of the full capabilities of the `Solution` struct. Simply define\n",
       "\n",
       "```julia\n",
       "using DifferentialEquations\n",
       "prob = continuousds.prob\n",
       "prob2 = remake(prob1; tspan=(0.0,2.0))\n",
       "sol = solve(prob2, Tsit5())\n",
       "# do stuff with sol...\n",
       "```\n",
       "\n",
       "The line `remake...` is necessary because by default the `tspan` of all problems ends at infinity. See the [remake documentation](http://docs.juliadiffeq.org/latest/basics/problem.html) for more info.\n",
       "\n",
       "## Relevant Functions\n",
       "\n",
       "[`trajectory`](@ref), [`jacobian`](@ref), [`dimension`](@ref), [`set_parameter!`](@ref).\n"
      ],
      "text/plain": [
       "```\n",
       "DynamicalSystem\n",
       "```\n",
       "\n",
       "The central structure of **DynamicalSystems.jl**. All functions of the suite that handle systems \"analytically\" (in the sense that they can use known equations of motion) expect an instance of this type.\n",
       "\n",
       "Contains a problem defining the system (field `prob`), the jacobian function (field `jacobian`) and the initialized Jacobian matrix (field `J`).\n",
       "\n",
       "## Constructing a `DynamicalSystem`\n",
       "\n",
       "```julia\n",
       "DiscreteDynamicalSystem(eom, state, p [, jacobian [, J0]]; t0::Int = 0)\n",
       "ContinuousDynamicalSystem(eom, state, p [, jacobian [, J0]]; t0 = 0.0)\n",
       "```\n",
       "\n",
       "with `eom` the equations of motion function. `p` is a parameter container, which we highly suggest to use a mutable object like `Array`, [`LMArray`](https://github.com/JuliaDiffEq/LabelledArrays.jl) or a dictionary. Pass `nothing` in the place of `p` if your system does not have parameters. With these constructors you also do not need to provide some final time, since it is not used by **DynamicalSystems.jl** in any manner.\n",
       "\n",
       "`t0`, `J0` allow you to choose the initial time and provide an initialized Jacobian matrix.\n",
       "\n",
       "Continuous system solvers use [**DifferentialEquations.jl**](http://docs.juliadiffeq.org/latest/) and by default are integrated with a 9th order Verner solver `Vern9()` with tolerances `abstol = reltol = 1e-9`.\n",
       "\n",
       "### Equations of motion\n",
       "\n",
       "The are two \"versions\" for `DynamicalSystem`, depending on whether the equations of motion (`eom`) are in-place (iip) or out-of-place (oop). Here is how to define them:\n",
       "\n",
       "  * **oop** : The `eom` **must** be in the form `eom(x, p, t) -> SVector` which means that given a state `x::SVector` and some parameter container `p` it returns an [`SVector`](http://juliaarrays.github.io/StaticArrays.jl/stable/pages/api.html#SVector-1) (from the [StaticArrays](https://github.com/JuliaArrays/StaticArrays.jl) module) containing the next state.\n",
       "  * **iip** : The `eom` **must** be in the form `eom!(xnew, x, p, t)` which means that given a state `x::Vector` and some parameter container `p`, it writes in-place the new state in `xnew`.\n",
       "\n",
       "`t` stands for time (integer for discrete systems). iip is suggested for big systems, whereas oop is suggested for small systems. The break-even point at around 100 dimensions, and for using functions that use the tangent space (like e.g. `lyapunovs` or `gali`), the break-even point is at around 10 dimensions.\n",
       "\n",
       "The constructor deduces automatically whether `eom` is iip or oop. It is not possible however to deduce whether the system is continuous or discrete just from the equations of motion, hence the 2 constructors.\n",
       "\n",
       "### Jacobian\n",
       "\n",
       "The optional argument `jacobian` for the constructors is a *function* and (if given) must also be of the same form as the `eom`, `jacobian(x, p, n) -> SMatrix` for the out-of-place version and `jacobian!(xnew, x, p, n)` for the in-place version.\n",
       "\n",
       "If `jacobian` is not given, it is constructed automatically using the module [`ForwardDiff`](http://www.juliadiff.org/ForwardDiff.jl/stable/).\n",
       "\n",
       "### Getting a `Solution` struct\n",
       "\n",
       "The continuous constructor creates a standard [ODEProblem](http://docs.juliadiffeq.org/latest/types/ode_types.html) from [**DifferentialEquations.jl**](http://docs.juliadiffeq.org/latest/).\n",
       "\n",
       "You can *always* take advantage of the full capabilities of the `Solution` struct. Simply define\n",
       "\n",
       "```julia\n",
       "using DifferentialEquations\n",
       "prob = continuousds.prob\n",
       "prob2 = remake(prob1; tspan=(0.0,2.0))\n",
       "sol = solve(prob2, Tsit5())\n",
       "# do stuff with sol...\n",
       "```\n",
       "\n",
       "The line `remake...` is necessary because by default the `tspan` of all problems ends at infinity. See the [remake documentation](http://docs.juliadiffeq.org/latest/basics/problem.html) for more info.\n",
       "\n",
       "## Relevant Functions\n",
       "\n",
       "[`trajectory`](@ref), [`jacobian`](@ref), [`dimension`](@ref), [`set_parameter!`](@ref).\n"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?DynamicalSystem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: \u001b[1mt\u001b[22m\u001b[1mr\u001b[22m\u001b[1ma\u001b[22m\u001b[1mj\u001b[22m\u001b[1me\u001b[22m\u001b[1mc\u001b[22m\u001b[1mt\u001b[22m\u001b[1mo\u001b[22m\u001b[1mr\u001b[22m\u001b[1my\u001b[22m\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "```\n",
       "trajectory(ds::DynamicalSystem, T [, u]; kwargs...) -> dataset\n",
       "```\n",
       "\n",
       "Return a dataset that will contain the trajectory of the system, after evolving it for total time `T`, optionally starting from state `u`. See [`Dataset`](@ref) for info on how to use this object.\n",
       "\n",
       "A `W×D` dataset is returned, with `W = length(t0:dt:T)` with `t0:dt:T` representing the time vector (*not* returned) and `D` the system dimension. For discrete systems both `T` and `dt` must be integers.\n",
       "\n",
       "## Keyword Arguments\n",
       "\n",
       "  * `dt` :  Time step of value output during the solving of the continuous system. For discrete systems it must be an integer. Defaults to `0.01` for continuous and `1` for discrete.\n",
       "  * `diff_eq_kwargs` : (only for continuous) A dictionary `Dict{Symbol, ANY}` of keyword arguments passed into the solvers of the [DifferentialEquations.jl](http://docs.juliadiffeq.org/latest/basics/common_solver_opts.html) package, for example `Dict(:abstol => 1e-9)`. If you want to specify a solver, do so by using the symbol `:solver`, e.g.: `Dict(:solver => DP5(), :maxiters => 1e9)`. This requires you to have been first `using OrdinaryDiffEq` to access the solvers. Defaults to `Dict(:solver => Vern9(), :abstol => 1e-9, :reltol => 1e-9)`, i.e. a 9th order Verner algorithm.\n"
      ],
      "text/plain": [
       "```\n",
       "trajectory(ds::DynamicalSystem, T [, u]; kwargs...) -> dataset\n",
       "```\n",
       "\n",
       "Return a dataset that will contain the trajectory of the system, after evolving it for total time `T`, optionally starting from state `u`. See [`Dataset`](@ref) for info on how to use this object.\n",
       "\n",
       "A `W×D` dataset is returned, with `W = length(t0:dt:T)` with `t0:dt:T` representing the time vector (*not* returned) and `D` the system dimension. For discrete systems both `T` and `dt` must be integers.\n",
       "\n",
       "## Keyword Arguments\n",
       "\n",
       "  * `dt` :  Time step of value output during the solving of the continuous system. For discrete systems it must be an integer. Defaults to `0.01` for continuous and `1` for discrete.\n",
       "  * `diff_eq_kwargs` : (only for continuous) A dictionary `Dict{Symbol, ANY}` of keyword arguments passed into the solvers of the [DifferentialEquations.jl](http://docs.juliadiffeq.org/latest/basics/common_solver_opts.html) package, for example `Dict(:abstol => 1e-9)`. If you want to specify a solver, do so by using the symbol `:solver`, e.g.: `Dict(:solver => DP5(), :maxiters => 1e9)`. This requires you to have been first `using OrdinaryDiffEq` to access the solvers. Defaults to `Dict(:solver => Vern9(), :abstol => 1e-9, :reltol => 1e-9)`, i.e. a 9th order Verner algorithm.\n"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?trajectory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "search: \u001b[1mD\u001b[22m\u001b[1ma\u001b[22m\u001b[1mt\u001b[22m\u001b[1ma\u001b[22m\u001b[1ms\u001b[22m\u001b[1me\u001b[22m\u001b[1mt\u001b[22m Abstract\u001b[1mD\u001b[22m\u001b[1ma\u001b[22m\u001b[1mt\u001b[22m\u001b[1ma\u001b[22m\u001b[1ms\u001b[22m\u001b[1me\u001b[22m\u001b[1mt\u001b[22m \u001b[1mD\u001b[22myn\u001b[1ma\u001b[22mmicalSys\u001b[1mt\u001b[22memsB\u001b[1ma\u001b[22m\u001b[1ms\u001b[22m\u001b[1me\u001b[22m @\u001b[1md\u001b[22m\u001b[1ma\u001b[22m\u001b[1mt\u001b[22meform\u001b[1ma\u001b[22mt_\u001b[1ms\u001b[22mtr\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "```\n",
       "Dataset{D, T} <: AbstractDataset{D,T}\n",
       "```\n",
       "\n",
       "A dedicated interface for datasets. It contains *equally-sized datapoints* of length `D`, represented by `SVector{D, T}`.\n",
       "\n",
       "When indexed with 1 index, a `dataset` is like a vector of datapoints.\n",
       "\n",
       "When indexed with 2 indices it behaves like a matrix that has each of the columns be the timeseries of each of the dynamic variables.\n",
       "\n",
       "## Description of indexing\n",
       "\n",
       "In the following let `i, j` be integers,  `typeof(data) <: AbstractDataset` and `v1, v2` be `<: AbstractVector{Int}` (`v1, v2` could also be ranges).\n",
       "\n",
       "  * `data[i]` gives the `i`th datapoint (returns an `SVector`)\n",
       "  * `data[v1]` will return a vector of datapoints\n",
       "  * `data[v1, :]` using a `Colon` as a second index will return a `Dataset` of these points\n",
       "  * `data[:, j]` gives the `j`th variable timeseries, as `Vector`\n",
       "  * `data[v1, v2]` returns a `Dataset` with the appropriate entries (first indices being \"time\"/point index, while second being dynamic variables)\n",
       "  * `data[i, j]` value of the `j`th variable, at the `i`th timepoint\n",
       "\n",
       "```\n",
       "\n",
       "Use `Matrix(dataset)` or `reinterpret(Matrix, dataset)` and `Dataset(matrix)` or `reinterpret(Dataset, matrix)` to convert. The `reinterpret` methods are cheaper but assume that each variable/timeseries is a *row* and not column of the `matrix`.\n",
       "\n",
       "If you have various timeseries vectors `x, y, z, ...` pass them like `Dataset(x, y, z, ...)`. You can use `columns(dataset)` to obtain the reverse, i.e. all columns of the dataset in a tuple.\n"
      ],
      "text/plain": [
       "```\n",
       "Dataset{D, T} <: AbstractDataset{D,T}\n",
       "```\n",
       "\n",
       "A dedicated interface for datasets. It contains *equally-sized datapoints* of length `D`, represented by `SVector{D, T}`.\n",
       "\n",
       "When indexed with 1 index, a `dataset` is like a vector of datapoints.\n",
       "\n",
       "When indexed with 2 indices it behaves like a matrix that has each of the columns be the timeseries of each of the dynamic variables.\n",
       "\n",
       "## Description of indexing\n",
       "\n",
       "In the following let `i, j` be integers,  `typeof(data) <: AbstractDataset` and `v1, v2` be `<: AbstractVector{Int}` (`v1, v2` could also be ranges).\n",
       "\n",
       "  * `data[i]` gives the `i`th datapoint (returns an `SVector`)\n",
       "  * `data[v1]` will return a vector of datapoints\n",
       "  * `data[v1, :]` using a `Colon` as a second index will return a `Dataset` of these points\n",
       "  * `data[:, j]` gives the `j`th variable timeseries, as `Vector`\n",
       "  * `data[v1, v2]` returns a `Dataset` with the appropriate entries (first indices being \"time\"/point index, while second being dynamic variables)\n",
       "  * `data[i, j]` value of the `j`th variable, at the `i`th timepoint\n",
       "\n",
       "```\n",
       "\n",
       "Use `Matrix(dataset)` or `reinterpret(Matrix, dataset)` and `Dataset(matrix)` or `reinterpret(Dataset, matrix)` to convert. The `reinterpret` methods are cheaper but assume that each variable/timeseries is a *row* and not column of the `matrix`.\n",
       "\n",
       "If you have various timeseries vectors `x, y, z, ...` pass them like `Dataset(x, y, z, ...)`. You can use `columns(dataset)` to obtain the reverse, i.e. all columns of the dataset in a tuple.\n"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "?Dataset"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 0.6.2",
   "language": "julia",
   "name": "julia-0.6"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "0.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
